Vance Martin

Econometric Modelling with Time Series (eBook, ePUB)

Specification, Estimation and Testing

• Format: ePub

Produktbeschreibung

This book provides a general framework for specifying, estimating and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalised method of moments estimation, nonparametric estimation and estimation by simulation. An important advantage of adopting the principle of maximum likelihood as the unifying framework for the book is that many of the estimators and test statistics proposed in econometrics can be derived within a likelihood framework, thereby providing a coherent vehicle for understanding their properties and interrelationships. In contrast to many existing econometric textbooks, which deal mainly with the theoretical properties of estimators and test statistics through a theorem-proof presentation, this book squarely addresses implementation to provide direct conduits between the theory and applied work.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 28. Dezember 2012
• Englisch
• ISBN-13: 9781139539531
• Artikelnr.: 52854241

Autorenporträt

Vance Martin is Professor of Econometrics at the University of Melbourne, Australia, a position he has held since 2000. He graduated with a PhD from Monash University in 1990. He was appointed Lecturer at the University of Melbourne in 1985 and became a Senior Lecturer in 1990.

Inhaltsangabe

Part I. Maximum Likelihood: 1. The maximum likelihood principle
2. Properties of maximum likelihood estimators
3. Numerical estimation methods
4. Hypothesis testing
Part II. Regression Models: 5. Linear regression models
6. Nonlinear regression models
7. Autocorrelated regression models
8. Heteroskedastic regression models
Part III. Other Estimation Methods: 9. Quasi-maximum likelihood estimation
10. Generalized method of moments
11. Nonparametric estimation
12. Estimation by stimulation
Part IV. Stationary Time Series: 13. Linear time series models
14. Structural vector autoregressions
15. Latent factor models
Part V. Non-Stationary Time Series: 16. Nonstationary distribution theory
17. Unit root testing
18. Cointegration
Part VI. Nonlinear Time Series: 19. Nonlinearities in mean
20. Nonlinearities in variance
21. Discrete time series models
Appendix A. Change in variable in probability density functions
Appendix B. The lag operator
Appendix C. FIML estimation of a structural model
Appendix D. Additional nonparametric results.